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      <title>如何处理消息堆积？ - 交互式测验</title>
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    <body>
      <div class="main-wrapper">
          <div class="quiz-container">
            <div class="quiz-header">
              <h1>如何处理消息堆积？ - 交互式测验</h1>
              <p>检验你对文档内容的理解程度</p>
            </div>
            <div id="quiz-content">
                <div class="quiz-body">
                    <div class="progress-bar">
                        <div id="progress-indicator"></div>
                    </div>
                    <div id="question-container">
                      <p id="question-text"></p>
                      <ul class="options-list" id="options-list"></ul>
                    </div>
                    <div id="result-container"></div>
                    <div id="hint-container" class="hint-container"></div>
                </div>
                <div class="quiz-footer" id="quiz-footer">
                  <button id="prev-btn" class="nav-btn">上一题</button>
                  <button id="hint-btn" class="nav-btn" style="background-color: #ffc107; color: #333; box-shadow: 0 4px 12px rgba(255,193,7,0.3);">提示</button>
                  <button id="next-btn" class="nav-btn">下一题</button>
                </div>
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      </div>

      <script>
        const quizData = [
  {
    "question": "根据文档，消息堆积最根本的原因是什么？",
    "options": [
      "分布式系统中存在网络延迟或带宽不足。",
      "生产者的消息产生速率超过了消费者的消息处理速率。",
      "消息队列本身的容量配置不当。",
      "消费端程序出现故障或不可用。"
    ],
    "correctAnswerIndex": 1,
    "explanation": "文档明确指出，消息堆积的根本原因可以归结为“生产者的消息产生速率超过了消费者的消息处理速率”。虽然消费者故障、配置不当或网络问题都是具体的成因，但它们最终都导致了消费速率低于生产速率这一根本性失衡。",
    "source": "文档中「消息堆积的根本原因分析」部分",
    "hint": "思考一下生产者和消费者之间最核心的速率关系是什么？"
  },
  {
    "question": "在检测消息堆积时，哪个监控指标最直接地反映了堆积的严重程度？",
    "options": [
      "消费者的CPU和内存使用率",
      "端到端延迟（End-to-End Latency）",
      "队列深度（Queue Depth）",
      "生产速率（Producer Rate）"
    ],
    "correctAnswerIndex": 2,
    "explanation": "文档指出，“队列深度（Queue Depth）”是消息堆积检测的核心指标，它直接指的是队列中未被消费的消息数量。当队列深度持续增加并超过阈值时，就意味着存在消息堆积。其他指标如生产速率和CPU使用率是分析原因的重要参考，但队列深度本身最直观地体现了堆积的量级。",
    "source": "文档中「消息堆积的检测与监控」-「1. 消息队列的长度监控」部分",
    "hint": "哪个指标像是一个水库的水位，直接告诉你积压了多少未处理的“水”？"
  },
  {
    "question": "当发现消费者处理消息过慢导致堆积时，以下哪项策略能最有效地提升消费端的整体吞吐量？",
    "options": [
      "在生产者端实现限流和熔断。",
      "采用批量消费（Batch Processing）的方式处理消息。",
      "对消息设置过期时间（TTL）。",
      "简化消息内容，减少消息大小。"
    ],
    "correctAnswerIndex": 1,
    "explanation": "批量消费能显著提高效率，因为它通过一次性处理多条消息，减少了与消息队列的交互次数和处理过程中的频繁I/O操作，从而提升整体吞吐量。而生产者限流、设置TTL虽然也是解决策略，但它们分别作用于生产端和丢弃策略，并非直接提升消费端吞吐量的核心方法。",
    "source": "文档中「解决消息堆积的策略」-「1. 提升消费者的消费能力」部分",
    "hint": "如何减少消费者与消息队列之间的网络交互次数，从而一次性处理更多工作？"
  },
  {
    "question": "在消息处理系统中，死信队列（Dead Letter Queue, DLQ）的主要作用是什么？",
    "options": [
      "用于临时缓存所有进入系统的消息，以实现削峰填谷。",
      "用于存放高优先级的消息，确保它们被优先处理。",
      "用于存放处理失败且超过最大重试次数的消息，以便进行后续的人工干预或分析。",
      "用于在消费者处理消息时进行备份，防止数据丢失。"
    ],
    "correctAnswerIndex": 2,
    "explanation": "文档描述，对于超过重试次数的消息，会自动转入死信队列。开发人员可以对死信队列中的消息进行手动处理或定期清理。这表明DLQ是处理那些“无法被正常消费”的最终消息的场所，而不是用于优先级、削峰或备份。",
    "source": "文档中「消息重试机制与丢弃策略」-「2. 常见的消息重试策略」部分",
    "hint": "当一条消息反复尝试处理都失败后，应该把它送到哪里去，而不是直接丢弃？"
  },
  {
    "question": "在设计消息重试机制时，为什么强调要保证“幂等性”（Idempotency）？",
    "options": [
      "为了提高消息处理的速度和系统吞吐量。",
      "为了让系统能够动态调整重试的间隔时间。",
      "为了避免因重复处理同一条消息而导致的数据不一致或业务逻辑错误。",
      "为了确保消息在传输过程中不会丢失。"
    ],
    "correctAnswerIndex": 2,
    "explanation": "文档在设计原则中首要强调了幂等性，并解释说“幂等性可以避免重复处理导致的数据不一致问题”。例如，一个扣款操作如果被重试多次，没有幂等性保证就会导致用户被重复扣款。这与消息不丢失（可靠性）、处理速度（性能）或重试间隔策略是不同的概念。",
    "source": "文档中「消息重试机制与丢弃策略」-「1. 消息重试机制的必要性与设计原则」部分",
    "hint": "如果一个“扣款10元”的消息被消费者处理了两次，会发生什么？如何防止这种情况带来的问题？"
  },
  {
    "question": "文档中提到的“指数退避重试”（Exponential Backoff）策略，其核心特点和优势是什么？",
    "options": [
      "每次重试的间隔时间会指数级增长，能有效减轻因频繁重试对下游系统造成的瞬时冲击。",
      "每次重试的间隔时间固定不变，实现简单。",
      "立即进行重试，适用于网络抖动等临时性问题。",
      "根据消息的优先级决定重试的间隔时间。"
    ],
    "correctAnswerIndex": 0,
    "explanation": "文档明确说明，指数退避重试的特点是“每次重试的时间间隔按照指数增长”，其优势在于“能有效减少重试操作对系统的冲击，适合需要逐渐恢复的场景”。这与固定间隔、立即重试或基于优先级的策略有本质区别。",
    "source": "文档中「消息重试机制与丢弃策略」-「2. 常见的消息重试策略」部分",
    "hint": "当下游服务可能需要一些时间来恢复时，哪种重试策略能给它越来越多的“喘息”时间？"
  },
  {
    "question": "根据文档，为消息设置过期时间（TTL, Time To Live）是一种怎样的丢弃策略，它最适用于哪类场景？",
    "options": [
      "是一种重试策略，消息过期后会被自动重新投递。",
      "是一种自动丢弃策略，适用于对实时性要求高且可以容忍数据丢失的场景。",
      "是一种手动丢弃策略，需要人工干预才能触发。",
      "适用于所有业务场景，是解决消息堆积的首选方案。"
    ],
    "correctAnswerIndex": 1,
    "explanation": "文档在“消息丢弃策略的设计与实现”一节中描述，TTL是让过期的消息被“自动丢弃”，并且“适用于对实时性要求高且可以容忍一定数据丢失的场景”。它是一种丢弃策略，而非重试策略，且并不适用于所有场景，尤其不适用于不能丢失数据的核心业务。",
    "source": "文档中「消息重试机制与丢弃策略」-「3. 消息丢弃策略的设计与实现」部分",
    "hint": "对于一些“时效性”很强的消息，比如“用户在线状态更新”，如果处理晚了，它还有意义吗？"
  }
];
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        let score = 0;
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            const totalQuestions = quizData.length;
            const answeredCount = userAnswers.filter(a => a !== null).length;
            const wrongCount = answeredCount - correctCount;
            const skippedCount = totalQuestions - answeredCount;
            const accuracy = totalQuestions > 0 ? Math.round((correctCount / totalQuestions) * 100) : 0;
            const errorRate = totalQuestions > 0 ? Math.round((wrongCount / totalQuestions) * 100) : 0;

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                '<h2>答题报告</h2>' +
                '<div class="score-summary">' +
                '<div class="score-card">' +
                '<h3>得分</h3>' +
                '<p>' + correctCount + ' / ' + totalQuestions + '</p>' +
                '</div>' +
                '<div class="score-card">' +
                '<h3>正确率</h3>' +
                '<p>' + accuracy + '%</p>' +
                '</div>' +
                '<div class="score-card">' +
                '<h3>错误率</h3>' +
                '<p>' + errorRate + '%</p>' +
                '</div>' +
                '<div class="score-card score-details">' +
                '<table>' +
                '<tr><td>正确</td><td>' + correctCount + '</td></tr>' +
                '<tr><td>错误</td><td>' + wrongCount + '</td></tr>' +
                '<tr><td>未答</td><td>' + skippedCount + '</td></tr>' +
                '</table>' +
                '</div>' +
                '</div>' +
                '<div class="report-actions">' +
                '<button id="review-btn">回顾测验</button>' +
                '<button class="primary" onclick="location.reload()">再试一次</button>' +
                '</div>' +
                '</div>';

            let reportEl = document.querySelector('.final-score-container');
            if (reportEl) {
                reportEl.remove();
            }
            quizContentEl.insertAdjacentHTML('beforeend', finalScoreHtml);
            document.getElementById('review-btn').addEventListener('click', reviewQuiz);
        }

        function reviewQuiz() {
            const reportEl = document.querySelector('.final-score-container');
            if (reportEl) {
                reportEl.remove();
            }

            document.querySelector('.quiz-body').style.display = 'block';
            document.getElementById('quiz-footer').style.display = 'flex';
            
            currentQuestionIndex = 0;
            loadQuestion();
            updateNavigationButtons(); // Ensure nav buttons are correctly displayed
        }

        // Event Delegation for options
        optionsListEl.addEventListener('click', (event) => {
            if (event.target && event.target.matches('li.option-item')) {
                // Check if the question has already been answered
                if (userAnswers[currentQuestionIndex] === null) {
                    const selectedIndex = parseInt(event.target.dataset.index, 10);
                    selectOption(selectedIndex);
                }
            }
        });

        prevBtn.addEventListener('click', goToPreviousQuestion);
        nextBtn.addEventListener('click', goToNextQuestion);
        hintBtn.addEventListener('click', showHint);
        
        // 初始加载
        loadQuestion();
      </script>
    </body>
    </html>
  